Mavatar's Disease Modeling Process

1. Data Foundation

We collect diverse transcriptomic data from GEO, CZ Cell x Gene Discover, and Single Cell Portal, applying rigorous quality control, annotation, and batch corrections to ensure consistency across studies.

2. Network Generation

DINA's advanced algorithms, including autoencoders, uncover complex gene-gene interactions and signaling pathways, creating tissue-specific (and soon cell-type-specific) networks.

3. Disease Characterization

By comparing diseased versus healthy profiles, we identify differentially expressed genes and cell-specific alterations, then map how these genes function collaboratively within disease-relevant pathways.

4. Digital Twin Creation

We synthesize these insights into comprehensive digital representations that mirror the complex molecular underpinnings of diseases. These models are continuously refined with new data and expanded to include proteomics for even broader perspective.

The Mavatar Advantage

Our data-driven, systems biology approach examines complete disease mechanisms rather than isolated genes, capturing tissue and cell-type specificity crucial for understanding disease variability. This process produces powerful disease models that support both our Discovery research platform and Precision clinical platform, advancing more personalized and effective treatment solutions.